[python]多线程快速入门
前言
线程是操作系统能够进行运算调度的最小单位，它被包含在进程之中，是进程中的实际运作单位。由于CPython的GIL限制，多线程实际为单线程，大多只用来处理IO密集型任务。
Python一般用标准库threading来进行多线程编程。
基本使用
- 方式1，创建
threading.Thread类的示例
import threading
import time
def task1(counter: int):
print(f"thread: {threading.current_thread().name}, args: {counter}, start time: {time.strftime('%F %T')}")
num = counter
while num > 0:
time.sleep(3)
num -= 1
print(f"thread: {threading.current_thread().name}, args: {counter}, end time: {time.strftime('%F %T')}")
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
# 创建三个线程
t1 = threading.Thread(target=task1, args=(7,))
t2 = threading.Thread(target=task1, args=(5,))
t3 = threading.Thread(target=task1, args=(3,))
# 启动线程
t1.start()
t2.start()
t3.start()
# join() 用于阻塞主线程, 等待子线程执行完毕
t1.join()
t2.join()
t3.join()
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
执行输出示例
main thread: MainThread, start time: 2024-10-26 12:42:37
thread: Thread-1 (task1), args: 7, start time: 2024-10-26 12:42:37
thread: Thread-2 (task1), args: 5, start time: 2024-10-26 12:42:37
thread: Thread-3 (task1), args: 3, start time: 2024-10-26 12:42:37
thread: Thread-3 (task1), args: 3, end time: 2024-10-26 12:42:46
thread: Thread-2 (task1), args: 5, end time: 2024-10-26 12:42:52
thread: Thread-1 (task1), args: 7, end time: 2024-10-26 12:42:58
main thread: MainThread, end time: 2024-10-26 12:42:58
- 方式2，继承
threading.Thread类，重写run()和__init__()方法
import threading
import time
class MyThread(threading.Thread):
def __init__(self, counter: int):
super().__init__()
self.counter = counter
def run(self):
print(f"thread: {threading.current_thread().name}, args: {self.counter}, start time: {time.strftime('%F %T')}")
num = self.counter
while num > 0:
time.sleep(3)
num -= 1
print(f"thread: {threading.current_thread().name}, args: {self.counter}, end time: {time.strftime('%F %T')}")
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
# 创建三个线程
t1 = MyThread(7)
t2 = MyThread(5)
t3 = MyThread(3)
# 启动线程
t1.start()
t2.start()
t3.start()
# join() 用于阻塞主线程, 等待子线程执行完毕
t1.join()
t2.join()
t3.join()
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
继承threading.Thread类也可以写成这样，调用外部函数。
import threading
import time
def task1(counter: int):
print(f"thread: {threading.current_thread().name}, args: {counter}, start time: {time.strftime('%F %T')}")
num = counter
while num > 0:
time.sleep(3)
num -= 1
print(f"thread: {threading.current_thread().name}, args: {counter}, end time: {time.strftime('%F %T')}")
class MyThread(threading.Thread):
def __init__(self, target, args: tuple):
super().__init__()
self.target = target
self.args = args
def run(self):
self.target(*self.args)
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
# 创建三个线程
t1 = MyThread(target=task1, args=(7,))
t2 = MyThread(target=task1, args=(5,))
t3 = MyThread(target=task1, args=(3,))
# 启动线程
t1.start()
t2.start()
t3.start()
# join() 用于阻塞主线程, 等待子线程执行完毕
t1.join()
t2.join()
t3.join()
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
多线程同步
如果多个线程共同对某个数据修改，则可能出现不可预料的后果，这时候就需要某些同步机制。比如如下代码，结果是随机的（个人电脑用python3.13实测结果都是0，而低版本的python3.6运行结果的确是随机的）
import threading
import time
num = 0
def task1(counter: int):
print(f"thread: {threading.current_thread().name}, args: {counter}, start time: {time.strftime('%F %T')}")
global num
for _ in range(100000000):
num = num + counter
num = num - counter
print(f"thread: {threading.current_thread().name}, args: {counter}, end time: {time.strftime('%F %T')}")
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
# 创建三个线程
t1 = threading.Thread(target=task1, args=(7,))
t2 = threading.Thread(target=task1, args=(5,))
t3 = threading.Thread(target=task1, args=(3,))
t4 = threading.Thread(target=task1, args=(6,))
t5 = threading.Thread(target=task1, args=(8,))
# 启动线程
t1.start()
t2.start()
t3.start()
t4.start()
t5.start()
# join() 用于阻塞主线程, 等待子线程执行完毕
t1.join()
t2.join()
t3.join()
t4.join()
t5.join()
print(f"num: {num}")
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
Lock-锁
使用互斥锁可以在一个线程访问数据时，拒绝其它线程访问，直到解锁。threading.Thread中的Lock()和Rlock()可以提供锁功能。
import threading
import time
num = 0
mutex = threading.Lock()
def task1(counter: int):
print(f"thread: {threading.current_thread().name}, args: {counter}, start time: {time.strftime('%F %T')}")
global num
mutex.acquire()
for _ in range(100000):
num = num + counter
num = num - counter
mutex.release()
print(f"thread: {threading.current_thread().name}, args: {counter}, end time: {time.strftime('%F %T')}")
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
# 创建三个线程
t1 = threading.Thread(target=task1, args=(7,))
t2 = threading.Thread(target=task1, args=(5,))
t3 = threading.Thread(target=task1, args=(3,))
# 启动线程
t1.start()
t2.start()
t3.start()
# join() 用于阻塞主线程, 等待子线程执行完毕
t1.join()
t2.join()
t3.join()
print(f"num: {num}")
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
Semaphore-信号量
互斥锁是只允许一个线程访问共享数据，而信号量是同时允许一定数量的线程访问共享数据。比如银行有5个窗口，允许同时有5个人办理业务，后面的人只能等待，待柜台有空闲才可以进入。
import threading
import time
from random import randint
semaphore = threading.BoundedSemaphore(5)
def business(name: str):
semaphore.acquire()
print(f"{time.strftime('%F %T')} {name} is handling")
time.sleep(randint(3, 10))
print(f"{time.strftime('%F %T')} {name} is done")
semaphore.release()
if __name__ == "__main__":
print(f"main thread: {threading.current_thread().name}, start time: {time.strftime('%F %T')}")
threads = []
for i in range(10):
t = threading.Thread(target=business, args=(f"thread-{i}",))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
print(f"main thread: {threading.current_thread().name}, end time: {time.strftime('%F %T')}")
执行输出
main thread: MainThread, start time: 2024-10-26 17:40:10
2024-10-26 17:40:10 thread-0 is handling
2024-10-26 17:40:10 thread-1 is handling
2024-10-26 17:40:10 thread-2 is handling
2024-10-26 17:40:10 thread-3 is handling
2024-10-26 17:40:10 thread-4 is handling
2024-10-26 17:40:15 thread-2 is done
2024-10-26 17:40:15 thread-5 is handling
2024-10-26 17:40:16 thread-0 is done
2024-10-26 17:40:16 thread-6 is handling
2024-10-26 17:40:19 thread-3 is done
2024-10-26 17:40:19 thread-4 is done
2024-10-26 17:40:19 thread-7 is handling
2024-10-26 17:40:19 thread-8 is handling
2024-10-26 17:40:20 thread-1 is done
2024-10-26 17:40:20 thread-9 is handling
2024-10-26 17:40:21 thread-6 is done
2024-10-26 17:40:23 thread-7 is done
2024-10-26 17:40:24 thread-5 is done
2024-10-26 17:40:24 thread-8 is done
2024-10-26 17:40:30 thread-9 is done
main thread: MainThread, end time: 2024-10-26 17:40:30
Condition-条件对象
Condition对象能让一个线程A停下来，等待其他线程，其他线程通知后线程A继续运行。
import threading
import time
import random
class Employee(threading.Thread):
def __init__(self, username: str, cond: threading.Condition):
self.username = username
self.cond = cond
super().__init__()
def run(self):
with self.cond:
print(f"{time.strftime('%F %T')} {self.username} 到达公司")
self.cond.wait() # 等待通知
print(f"{time.strftime('%F %T')} {self.username} 开始工作")
time.sleep(random.randint(1, 5))
print(f"{time.strftime('%F %T')} {self.username} 工作完成")
class Boss(threading.Thread):
def __init__(self, username: str, cond: threading.Condition):
self.username = username
self.cond = cond
super().__init__()
def run(self):
with self.cond:
print(f"{time.strftime('%F %T')} {self.username} 发出通知")
self.cond.notify_all() # 通知所有线程
time.sleep(2)
if __name__ == "__main__":
cond = threading.Condition()
boss = Boss("老王", cond)
employees = []
for i in range(5):
employees.append(Employee(f"员工{i}", cond))
for employee in employees:
employee.start()
boss.start()
boss.join()
for employee in employees:
employee.join()
执行输出
2024-10-26 21:16:20 员工0 到达公司
2024-10-26 21:16:20 员工1 到达公司
2024-10-26 21:16:20 员工2 到达公司
2024-10-26 21:16:20 员工3 到达公司
2024-10-26 21:16:20 员工4 到达公司
2024-10-26 21:16:20 老王 发出通知
2024-10-26 21:16:20 员工4 开始工作
2024-10-26 21:16:23 员工4 工作完成
2024-10-26 21:16:23 员工1 开始工作
2024-10-26 21:16:28 员工1 工作完成
2024-10-26 21:16:28 员工2 开始工作
2024-10-26 21:16:30 员工2 工作完成
2024-10-26 21:16:30 员工0 开始工作
2024-10-26 21:16:31 员工0 工作完成
2024-10-26 21:16:31 员工3 开始工作
2024-10-26 21:16:32 员工3 工作完成
Event-事件
在 Python 的 threading 模块中，Event 是一个线程同步原语，用于在多个线程之间进行简单的通信。Event 对象维护一个内部标志，线程可以使用 wait() 方法阻塞，直到另一个线程调用 set() 方法将标志设置为 True。一旦标志被设置为 True，所有等待的线程将被唤醒并继续执行。
Event 的主要方法
set()：将事件的内部标志设置为True，并唤醒所有等待的线程。clear()：将事件的内部标志设置为False。is_set()：返回事件的内部标志是否为True。wait(timeout=None)：如果事件的内部标志为False，则阻塞当前线程，直到标志被设置为True或超时（如果指定了timeout）。
import threading
import time
import random
class Employee(threading.Thread):
def __init__(self, username: str, cond: threading.Event):
self.username = username
self.cond = cond
super().__init__()
def run(self):
print(f"{time.strftime('%F %T')} {self.username} 到达公司")
self.cond.wait() # 等待事件标志为True
print(f"{time.strftime('%F %T')} {self.username} 开始工作")
time.sleep(random.randint(1, 5))
print(f"{time.strftime('%F %T')} {self.username} 工作完成")
class Boss(threading.Thread):
def __init__(self, username: str, cond: threading.Event):
self.username = username
self.cond = cond
super().__init__()
def run(self):
print(f"{time.strftime('%F %T')} {self.username} 发出通知")
self.cond.set()
if __name__ == "__main__":
cond = threading.Event()
boss = Boss("老王", cond)
employees = []
for i in range(5):
employees.append(Employee(f"员工{i}", cond))
for employee in employees:
employee.start()
boss.start()
boss.join()
for employee in employees:
employee.join()
执行输出
2024-10-26 21:22:28 员工0 到达公司
2024-10-26 21:22:28 员工1 到达公司
2024-10-26 21:22:28 员工2 到达公司
2024-10-26 21:22:28 员工3 到达公司
2024-10-26 21:22:28 员工4 到达公司
2024-10-26 21:22:28 老王 发出通知
2024-10-26 21:22:28 员工0 开始工作
2024-10-26 21:22:28 员工1 开始工作
2024-10-26 21:22:28 员工3 开始工作
2024-10-26 21:22:28 员工4 开始工作
2024-10-26 21:22:28 员工2 开始工作
2024-10-26 21:22:30 员工3 工作完成
2024-10-26 21:22:31 员工4 工作完成
2024-10-26 21:22:31 员工2 工作完成
2024-10-26 21:22:32 员工0 工作完成
2024-10-26 21:22:32 员工1 工作完成
使用队列
Python的queue模块提供同步、线程安全的队列类。以下示例为使用queue实现的生产消费者模型
import threading
import time
import random
import queue
class Producer(threading.Thread):
"""多线程生产者类."""
def __init__(
self, tname: str, channel: queue.Queue, done: threading.Event
):
self.tname = tname
self.channel = channel
self.done = done
super().__init__()
def run(self) -> None:
"""Method representing the thread's activity."""
while True:
if self.done.is_set():
print(
f"{time.strftime('%F %T')} {self.tname} 收到停止信号事件"
)
break
if self.channel.full():
print(
f"{time.strftime('%F %T')} {self.tname} report: 队列已满, 全部停止生产"
)
self.done.set()
else:
num = random.randint(100, 1000)
self.channel.put(f"{self.tname}-{num}")
print(
f"{time.strftime('%F %T')} {self.tname} 生成数据 {num}, queue size: {self.channel.qsize()}"
)
time.sleep(random.randint(1, 5))
class Consumer(threading.Thread):
"""多线程消费者类."""
def __init__(
self, tname: str, channel: queue.Queue, done: threading.Event
):
self.tname = tname
self.channel = channel
self.done = done
self.counter = 0
super().__init__()
def run(self) -> None:
"""Method representing the thread's activity."""
while True:
if self.done.is_set():
print(
f"{time.strftime('%F %T')} {self.tname} 收到停止信号事件"
)
break
if self.counter >= 3:
print(
f"{time.strftime('%F %T')} {self.tname} report: 全部停止消费"
)
self.done.set()
continue
if self.channel.empty():
print(
f"{time.strftime('%F %T')} {self.tname} report: 队列为空, counter: {self.counter}"
)
self.counter += 1
time.sleep(1)
continue
else:
data = self.channel.get()
print(
f"{time.strftime('%F %T')} {self.tname} 消费数据 {data}, queue size: {self.channel.qsize()}"
)
time.sleep(random.randint(1, 5))
self.counter = 0
if __name__ == "__main__":
done_p = threading.Event()
done_c = threading.Event()
channel = queue.Queue(30)
threads_producer = []
threads_consumer = []
for i in range(8):
threads_producer.append(Producer(f"producer-{i}", channel, done_p))
for i in range(6):
threads_consumer.append(Consumer(f"consumer-{i}", channel, done_c))
for t in threads_producer:
t.start()
for t in threads_consumer:
t.start()
for t in threads_producer:
t.join()
for t in threads_consumer:
t.join()
线程池
在面向对象编程中，创建和销毁对象是很费时间的，因为创建一个对象要获取内存资源或其他更多资源。在多线程程序中，生成一个新线程之后销毁，然后再创建一个，这种方式就很低效。池化多线程，也就是线程池就为此而生。
将任务添加到线程池中，线程池会自动指定一个空闲的线程去执行任务，当超过最大线程数时，任务需要等待有新的空闲线程才会被执行。Python一般可以使用multiprocessing模块中的Pool来创建线程池。
import time
from multiprocessing.dummy import Pool as ThreadPool
def foo(n):
time.sleep(2)
if __name__ == "__main__":
start = time.time()
for n in range(5):
foo(n)
print("single thread time: ", time.time() - start)
start = time.time()
t_pool = ThreadPool(processes=5) # 创建线程池, 指定池中的线程数为5(默认为CPU数)
rst = t_pool.map(foo, range(5)) # 使用map为每个元素应用到foo函数
t_pool.close() # 阻止任何新的任务提交到线程池
t_pool.join() # 等待所有已提交的任务完成
print("thread pool time: ", time.time() - start)
线程池执行器
python的内置模块concurrent.futures提供了ThreadPoolExecutor类。这个类结合了线程和队列的优势，可以用来平行执行任务。
import time
from random import randint
from concurrent.futures import ThreadPoolExecutor
def foo() -> None:
time.sleep(2)
return randint(1,100)
if __name__ == "__main__":
start = time.time()
futures = []
with ThreadPoolExecutor(max_workers=5) as executor:
for n in range(10):
futures.append(executor.submit(foo)) # Fan out
for future in futures: # Fan in
print(future.result())
print("thread pool executor time: ", time.time() - start)
执行输出
44
19
86
48
35
74
59
99
58
53
thread pool executor time: 4.001955032348633
ThreadPoolExecutor类的最大优点在于：如果调用者通过submit方法把某项任务交给它执行，那么会获得一个与该任务相对应的Future实例，当调用者在这个实例上通过result方法获取执行结果时，ThreadPoolExecutor会把它在执行任务的过程中所遇到的异常自动抛给调用者。而ThreadPoolExecutor类的缺点是IO并行能力不高，即便把max_worker设为100，也无法高效处理任务。更高需求的IO任务可以考虑换异步协程方案。
参考
- 郑征《Python自动化运维快速入门》清华大学出版社
- Brett Slatkin《Effective Python》(2nd) 机械工业出版社